The coronavirus outbreak has had an unprecedented impact on organisations, their operations and the management of workforces. With some needing to respond to substantially increased demand and others facing a situation where customer demand has virtually stopped, the extremes of the impact are clear. When combined with the natural uncertainty and anxiety that everyone will have, this has created a scenario that has never been faced in modern times.
Our partner Ember is seeing this unfold and helping support our customers respond to this crisis. We have been inspired by the effort, innovation, commitment and resilience being shown as teams rally around, often for the first time, to get things done; and in response, adapt their support for customers and their teams.
This event is designed to share our experience, which we are seeing across the market. The webinar will be hosted Mike Havard, Chairman of Ember Group and Carolyn Blunt, Director of Learning Solutions who will share examples and learnings from the market on how organisations are adapting their operating model, establishing new ways of working, innovating by using digital and automation technologies, and critically, preparing to operate differently for an as-yet-uncertain future.
We’d love to have you join us in this event and share your own experiences, as this is one of the ways we can all help each other to get through this crisis. The webinar is being held on 9th April at 2pm (GMT), and all you need to do to register is click on the link below.
Through the app Warwick Analytics will apply its Machine Learning-based interaction analytics to unstructured customer data within support tickets whether they come via chat, web forms or email.
The full topics, sentiment and emotional intents of the contact will be automatically analysed and classified, accurately in near real time. This saves the call center or helpdesk having to classify each ticket manually, or using keyword classification which can be inaccurate.
Users will be able to set alerts for the early warnings of issues and complaints so they can be triaged, and where necessary prioritised and escalated fast. This means that an issue that could otherwise become a brand-affecting event, such as a serious customer issue that might otherwise end up on social media, can be dealt with by the right person at the right time.
The PrediCX app also features Multi-label Capability which means it can identify and classify multiple topics, sentiments and intents within a single piece of customer feedback, something that is often missed with human or generic ML classification.
Dan Somers, CEO at Warwick Analytics adds: “With the new app Zendesk users can analyse customer interactions across all customer touch-points, and use the insight to define and optimise support strategies. Helpdesks will be able to improve the speed of resolution, provide more relevant responses and streamline their chat optimisation process.”
Lee Mostari, Director of Insights & Analytics at Ember, a partner of Warwick Analytics, adds: “The world is becoming faster-moving with consumers demanding quicker service and more transparency than ever before. This Zendesk App enables brands to deal with the right queries in the right order and optimise the customer experience. Not only does this optimise customer operations, but it helps to protect and maintain the brand as well to enhance customer advocates and minimise customer detraction which from digital customers can both quickly amplify on social media.”
Analyse customer frustrations and the failure within each channel e.g. containment and the root cause of switch. That’s going to give you the right insight to reduce friction.
Identify the insights into what customers are trying to achieve in the channel and help build that roadmap to change. So if customers are telling you they’re trying to do certain transactions in a certain way, capitalize on that to get the best bang for your buck from the digital roadmap.
Prioritize changes that deliver both an improved CX as well as reducing operational costs. You’ll get a quicker payback to fund more change.
Work to reduce the channel silos and reduce customer friction. A typical target operating model to achieve this is between one and three years so this will take time but start none the less and you will realize benefits and reduce channel friction as you go through that journey.
Aim for an omni-channel experience in which every customer knows what channel to use for what transaction and every interaction is handled correctly first time, Again this will take time and may not by 100pc achievable but let’s make inroads to try to move to that space.
Be prepared to be overwhelmed for a short period of time – by the data and by the possibilities. Work with a partner who can help to make a quick difference, because otherwise PRCs will just become science experiments and a reason to not do things. Making a small thing successful to start with is always better than anything else.
Welcome to the latest edition of the Supermarket Social report series, focussing on the 2019 Christmas period. The analysis within the report is brought to you in conjunction with our partners Solutions for Retail Brands (S4RB)
In this edition we discuss some of the trends uncovered from Twitter during the Christmas 2019 trading period (15th November 2019 – 15th January 2020) pertaining to the big six UK Grocery retailers : Asda, Coop, Sainsburys, Morrisons, Tesco and Waitrose.
Using the Warwick Analytics PrediCX engine, Twitter chatter mentioning the above retailers was labelled and categorised, enabling S4RB to apply their Own Brand industry expertise to identify several key themes in the market including: Meat-free Meat-free products continue to grow in popularity, Christmas-online The Christmas online shop is a key battleground and Tracking Tracking competitors can avoid product incidents.
In the 4th of our Omni-channel Series of blogs we look at why your omni-channel experience might be failing. It’s likely that you will be receiving customer feedback through a growing number of different channels such as surveys, customer conversations from interactions, or insights from customer actions – or how Garter describe it: Direct, Indirect and Inferred customer feedback. But as you try and provide a customer experience that operates consistently and efficiently across all channels there are some common barriers that might be getting in the way of success.
No empowered CX leader
Only 30% of organisations have an executive on the board representing CX. We think this is very low as we believe not having a single owner has a high risk of creating channel silos.
For example, we quite often see web self-service being owned by somebody different to telephony and customer service, with different agendas and different objectives. And that can lead to some friction.
Paradoxically, 9 out of 10 organisations see customer experience as a competitive differentiator. But if only 30% have an executive responsible for CX on the board, how are they going to deliver great customer experience with a silo channel approach? There’s certainly a high risk of failure there.
Furthermore, probably less than half of the 30% may not be empowered CX leaders. In other words, even if there’s someone responsible for CX, actually having the capability to affect cross functional change is easier said than done – some boards are just more receptive to empowering individuals. Consider whether your CX leader is as empowered as they could be.
Channel failure driving interactions
We found that 40-70% of interactions were driven from some sort of failure in the originating channel. In particular, 25-40% was driven from web self-service. This means customers have tried a cheap service channel in the first instance and then ended up switching to a more expensive channel to get their query resolved…that’s a massive amount of unnecessary demand and cost.
Although many organisations acting on the drivers of failure demand have delivered operational savings of around 11-20% this is still way below expectations and there is plenty of opportunity to improve, particularly if up to 70% of contact is waste!
Channels are not connected
Only 8.4% of organisations have every channel connected. For the other 92%, this is inevitably going to lead to friction because they’re not aligned. That runs the risk of some customer dissatisfaction and so we’re seeing an increasing number of organisations who are actively working towards a truly connected channel strategy.
Let’s start with what an omni-channel experience SHOULDN’T look like, most likely commonplace in the majority of organisations. We call it the hourglass shape and hopefully you can see why (see fig below).
What we see is a fat self-serve at the bottom which is good as it’s the cheapest mode. Chatbots are being used to field a lot this section off. But then live chat is not being used as strategically as it could be as the next level up. All too often customers end up coming back to use Voice as a channel, often through channel failure or switching which is expensive and bad for customer experience. In this model everything can be seen as trying to deflect from voice and First-Call resolution (FCR), as opposed to being a joined up strategy.
Even though Voice is the most complex and the most expensive channel, typically 70% of traffic is exactly that. Ideally Voice should deal mostly with the most complex queries.
Now let’s look at the utopia, how your omni-channel experience should look like.
– You would have the biggest number of transactions going through self service
– Then information requests or simple questions would be handled via a chatbot so no need to have an expensive agent conversation – and its accurate and optimised!
– You would offer a live chat provision to help with operational contact management (so concurrent customer conversations) where there is some complexity so requires an assisted channel but is handled efficiently
– And finally, traditional telephony agent conversations. These would be customers seeking guidance on what product to buy or maybe highly emotional conversations requiring a human interaction
In this utopia state, every customer knows what channel to use for what transaction, every interaction is handled correctly first time, there is no failure and the world is lovely.
OK, so we now know the utopian vision that we should strive for but the reality is perhaps something like this, which we call more of a target state.
There will be an element of failure, represented in red, but this would be significantly reduced in a more joined up approach.
The start point for many organisations is essentially delivering the different channel options. And we know, a lot of organizations are still yet to go on the chatbot journey, and some don’t have live chat.
The aim is a more joined up approach, understanding what is failing within each channel, and having the sort of the insights and the analytics to help improve and reduce that non first contact resolution. This is a great step forward and what we should be striving to achieve.
You are likely to have a target to increase NPS or some similar metric for satisfaction and potentially customer loyalty within your business – this is a common use case for us and we have helped many clients increase satisfaction by 20% or more by helping to find drivers and root causes of customer dissatisfaction and setting an action plan for improvement – in many cases this is linked to a poor channel experience so getting this right can pay dividends.
For many clients we focus on understanding the drivers of dissatisfaction typically driven from channel shift and increased effort from customers. Understanding those insights leads to a pretty significant improvement in MPs. We would typically see between 10 and 20 point improvement by acting on those drivers of dissatisfaction.
Knowing the amount of channel failure
It’s really important to understand the amount of failure that you’re handling to help inform your channel strategy. Using analytics you can do this by:
Isolating contact where the customer mentioned that they tried to complete something in a different channel – frighteningly we have seen this number to be nearly half of the context we analyse;
Identifying the processes behind the root cause drivers of failure. Prioritise a set of actions that the organisation should take to help reduce that channel failure.
By carrying out these two actions you could easily achieve between a 5 and 7% reduction in headcount which could equate to around 150 FTE. A huge business benefit from the insights and if there’s a very high failure rate, there’s much more to go after too.
Understanding the types of demand you are handling
To understand the different types of demands being handled within a contact centre, you first determine whether a transaction is a value to the customer or an irritant to the customer.
And then do the same from your organization’s view i.e. is the transaction a value to you or not.
You can then plot these on a matrix. In this example (figure 1), the organisation was handling more than half (54%) of transactions that were of value to the customer but an irritant to the organisation. So these are prime for what we call automated drive self-service and that creates a massive opportunity for operational efficiency by maintaining a great customer experience, but freeing up that expensive resource in the contact centre.
Previously only 3% of the cases agents promoted self service to the customer but once they understood the value irritants and coached agents to promote self service, the number rose to over 60% in just 6 weeks.
Best of all, there was a massive impact in the contact volume with a 30% reduction in the contact centre
More accurate classification
When agents are manually classifying all of the data after every call, a lot of these classified labels are not actually very helpful. We call them bucket categories, because they are literally another category, which can be up to about 20%.
And there are also pseudo bucket categories, which is when two smart agents are doing what they think is right, but actually they’re classifying things differently. The class becomes confused and becomes unactionable. The organisations ability to develop their customer experience is limited because the analytics is just not granular or accurate enough.
By automating the classification or labelling, these bucket categories are removed and you start to see things that you may have missed from an early warning point of view that you wouldn’t have otherwise spotted. We call it a back cast.
And the nice thing is, when you’re automating all of that human activity, you’re taking away sometimes 10% of the work from those human agents as well as providing better insight.
Whatever methods that you, or your analytics partner choose, always look at how the customer is feeling in the beginning and then look at what the agents do during the interaction to make them feel happier. To achieve this it can be very powerful to look at the topics or emotional intent within the conversation. With the right analytics this can be done automatically and precisely – there’s no guessing what the customers are telling you or how they’re telling you. As Bill Gates said: “If you want to learn about the business, you’ll learn the most from your unhappiest customers.”
Now we’re not suggesting that you deliberately make your customers unhappy in order to retrieve information. But it is ironic that organisations tend to send follow up surveys people get very fatigued about, just to understand how a conversation went.
A lot of the time the customer is clearly telling you how they are feeling or what their intent is – you just need the right analytics to pick these sentiments up and the need for the survey is removed.
For example, if a customer is actually telling you they’ve switched channel you can look at the meta data of the topics to get a much more accurate and actionable qualitative view about that issue. Or conversely, if they have come over to a different channel you might not have picked it up in your initial FCR analysis.
Being able to isolate those comments where your customers are really telling you these things is really powerful. Yes, it’s a motive. Yes, no one likes to hit bad news. But it’s all there. And if you let it speak to you, you can follow these pathways, see which are the big ones and tackle things in the priority order to be able to drive the quickest improvements.
There’s always a lot of low hanging fruit when you can effectively and accurately identify multiple intents across an omni-channel experience. Because when you know what you didn’t know before, then you can go after it quickly.
In this series of blogs, we have teamed up with our partner Ember to cover everything there is to know when it comes to offering a true omni-channel customer experience. From why you should have one, what it should look like and what you might be getting wrong. Let’s start with ‘Why you should adopt an omni-channel customer experience’.
Customers today expect to easily engage with brands across multiple touchpoints. They also expect their experience of an organisation to be consistent across the different channels. Therefore, it’s vital that channels are integrated to provide the best experience for consumers.
Being truly Omni-channel also allows businesses to have one single view of the customer so they can integrate feedback from different sources into one platform. A contact center can then benefit from insights that will reduce channel switching and push customers to the most cost-effective channel that is right for them.
Warwick Analytics can show you which parts of your omni-channel strategy can be improved, where customers are switching, what your customers are telling you and where you can make operational efficiencies, all whilst improving your customer experience.
Here are two of the top use cases for adopting a true omni-channel experience:
Improve operational efficiency
Around 95% of the organisations we talk to have a need to improve operational efficiency in some way. Omni-channel analytics provide a clear way to achieve that.
Whether it’s reducing channel failure, channel switch or reducing overall contact volume, having a single overarching view of all your channels, including the demand drivers, will give you a much better chance of improving efficiencies.
Improve customer experience and customer retention
We all want to improve customer experience and customer retention. If we keep customers happy they stay longer, spend more, and tell more people about the experience. Therefore the use case is:
Identify which channels your customers are best suited to – and which work best for specific types of interaction;
Understand the causes of channel failure and what drives customers to switch;
Reduce customer effort by delivering service in the customer’s preferred channel first-time.
Next time we look at 5 strong use cases for omni-channel analytics.
On our latest guest blog for partner S4RB we take some publicly available Tweets looking specifically at packaging. It’s a timely example to use as it is a growing topic as consumers voice more and more environmental concerns, as well as the usual quality issues relating to packaging.
In a generic text analysis model, you might be lucky to pick up packaging issues at all, or at best to pick them up and assign positive or negative sentiment. However, this doesn’t help to action anything, not without further reading and coding. One must also figure out what the themes are to code in the first place.
With ‘human-in-the-loop’ software like PrediCX and the S4RB model specific to grocery retailer, the new signals are referred to a human as they appear so that nothing is missed, nor does it have to be guessed. The data truly speaks for itself!
@Tesco check this out! Bacon I can open one handed. Unlike your packaging that I have to attack with a knife because for years the pull tab has not worked once. @AldiUK #voodoomagic #bacon
Beyond sentiment this richness allows brand owners to understand competitive advantage or disadvantages, which can feed into either marketing or product development. The ease of opening on Aldi’s product will be for more than just bacon!
Also, there’s a hint of long-standing Tesco customer so can add label: “loyal customer”. These tags can both be used to help improve packaging, avoid serious issues, and also improve the brand’s standing to competitors in terms of the features that customers mention. What’s interesting is that a longstanding, loyal Tesco shopper has made an unsolicited comment to Tesco about a competitor. Have they switched? Imploring their favoured brand to improve?
In a large company an internal helpdesk can be as large and complex as any external CRM and keeping down costs whilst keeping up service levels are high priorities.
How PrediCX was applied to ServiceNow data
The customer was an enterprise providing software and consultancy with around 5,000 employees and around 30,000 tickets per year. Warwick Analytics applied its PrediCX software to the helpdesk tickets from ServiceNow (it can also work with BMC Remedy, Zendesk, Salesforce and others). After a short training period of a couple of days, PrediCX was already classifying unstructured data i.e. the text notes within the tickets as well as the notes of the solutions and corrective actions. There were several use cases:
• Early warning of issues and hints for root causes to minimise risk and lost productivity
• Root causes of common issues to obviate tickets and cost, and improve service levels
• Identify opportunities for automation and self-serve both direct and to support agents
• Automated, accurate classification
The view was both retrospective and forward-looking, i.e. to identify the opportunities it could have saved in the past had it been implemented at the time, as well as identifying opportunities going forward.
What PrediCX found
The support teams spend much of their time dealing with these situations rather than enhancing the service. Whilst there will always be emergency situations, the opportunities are to spot the common issues as quickly as possible with alerts, as well as identifying the root causes from the notes of investigated tickets. This can isolate the relevant failure mode quickly and hint at the corrective action required, as well as identifying whether the issue is new or a repeat of something in the past. This is compared to the alternative of manually classification which does not pick out the rich detail of the ticket symptoms (or solutions) and is often inconsistent and inaccurate.
Early warning of payroll issues – The insight allows managers to quickly see when certain failure modes are reappearing e.g. the Expense error FM2 which reoccurred from the first incident on 16 March 2018 and reappeared on 4 May 2018. If PrediCX had been used at the time, it would have helped to implement a permanent fix during the first incident. It also would have shrunk the time of impact of issues by providing the earliest warning of an issue and hints at root causes e.g. the Submission error FM2 from 4 March 2018 to 25 March 2018. PrediCX can help obviate future failure modes and facilitate projects to implement preventative and corrective actions that can be executed ‘offline’ without disrupting service levels.
Hidden laptop issues – the analysis revealed a driver incompatibility issue that took 9 months to resolve. With PrediCX, it’s easy to see that it correlates with a particular Windows error and memory error too. Alert triggers within PrediCX would have picked this up.
Opportunities for automation and deflection – PrediCX looked at tickets over a period of 14.5 months and provided an analysis of whether issues are to do with staff growth and activity, wear & tear (for hardware), a repeating issue which can be deflected (i.e. estimates based on common root causes), a repeated issue which can’t be deflected (i.e. where no common root causes) and issues which appear to be non-repeating. These hint at the potential opportunities for deflection and automation. It shows that 25% of the total tickets analysed
could have been deflected and 29% could be automated to some degree. Given there are about 40,000 tickets per annum and based on a typical cost of solving a ticket, it is estimated that PrediCX identified savings of around a third of the cost of the helpdesk. There may be further opportunities to save on wear and tear too, e.g. by further insight into the supply chain and whether alternative suppliers or processes can prolong the life of assets. There are also opportunities to classify the rest of the tickets automatically and more consistently which leads to more accurate triage and resolution.
Warwick Analytics is able to generate actionable insight and automation at both a strategic and tactical level for helpdesks. It enables helpdesks to optimise their costs whilst maintaining service levels to meet the expectations of their internal customers.